Model-Based Prediction of Phase III Overall Survival in Colorectal Cancer on the Basis of Phase II Tumor Dynamics

Claret, Laurent; Girard, Pascal; Hoff, Paulo M.; Cutsem, Éric Van; Zuideveld, Klaas P.; Jorga, Karin; Fagerberg, Jan; Bruno, René

doi:10.1200/jco.2008.21.0807

Cited by 242 publications

(334 citation statements)

References 17 publications

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“…The final model integrated a second direct effect on the tumor volume P , which can be attributed to a cytotoxic effect of the drug. In addition, in line with previous suggestions,8 we added a resistance term (δ) that decreases the cytotoxic effect on tumor volume with time (Eq. (1)).…”

Section: Resultsmentioning

confidence: 93%

“…Nevertheless, regarding BIC values, no significant improvement was observed between the dual effect model and the single cytotoxic effect model or the model described in ref. 8 ( Supplementary Table S1 ). The values of the parameter estimated by the final model in both preclinical and clinical settings are summarized in Table 1.…”

Section: Resultsmentioning

confidence: 99%

“…The term representing drug‐induced decay can be constant or exponential, driven by drug exposure, and it can incorporate a resistance term or a delay term to accommodate a wide range of tumor response shapes (see refs. 8, 9, 19, 20, 21, 22, 23, 24 for reviews).…”

Section: Methodsmentioning

confidence: 99%

“…Houk et al 7. used the model of Claret et al 8. to describe the efficacy of sunitinib in metastatic RCC patients, and Stein et al 9.…”

mentioning

confidence: 99%

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Preclinical Modeling of Tumor Growth and Angiogenesis Inhibition to Describe Pazopanib Clinical Effects in Renal Cell Carcinoma

Ouerdani

Struemper

Suttle

et al. 2015

CPT Pharmacometrics Syst. Pharmacol.

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The objective was to leverage tumor size data from preclinical experiments to propose a model of tumor growth and angiogenesis inhibition for the analysis of pazopanib efficacy in renal cell carcinoma (RCC) patients. We analyzed tumor data in mice with RCC CAKI‐2 cell line treated with pazopanib. Clinical tumor size data obtained in a subset of patients with RCC were also analyzed. A model accounting for the processes of tumor growth, angiogenesis, and drug effect was developed. The final tumor model was composed of two variables: the tumor and its vasculature. Our results show that, both in mice and in humans, pazopanib exhibits a dual mechanism of action, and parameter estimation values highlight the inherent difference between mice and humans on the time scale of tumor size response. We developed a semimechanistic tumor growth inhibition model that takes into account tumor angiogenesis in order to describe the effects of pazopanib in mice. Analyzing rich preclinical data with a semimechanistic model may be a relevant approach to facilitate the description of sparse clinical longitudinal tumor size data and to provide insights for the understanding of the drug mechanisms of action in patients.

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Section: Resultsmentioning

confidence: 93%

Section: Resultsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

“…Houk et al 7. used the model of Claret et al 8. to describe the efficacy of sunitinib in metastatic RCC patients, and Stein et al 9.…”

mentioning

confidence: 99%

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Preclinical Modeling of Tumor Growth and Angiogenesis Inhibition to Describe Pazopanib Clinical Effects in Renal Cell Carcinoma

Ouerdani

Struemper

Suttle

et al. 2015

CPT Pharmacometrics Syst. Pharmacol.

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“…Added benefits have been gained from mathematical models of tumor size (TS) dynamics4, 5, 6 and tumor growth inhibition,7, 8, 9 which can characterize anticancer drug effects over time and provide improved predictors of long‐term clinical outcomes 10, 11…”

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confidence: 99%

Assessing Similarity Among Individual Tumor Size Lesion Dynamics: The CICIL Methodology

Terranova

Girard

Ιωάννου

et al. 2018

CPT Pharmacom & Syst Pharma

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Mathematical models of tumor dynamics generally omit information on individual target lesions (iTLs), and consider the most important variable to be the sum of tumor sizes (TS). However, differences in lesion dynamics might be predictive of tumor progression. To exploit this information, we have developed a novel and flexible approach for the non‐parametric analysis of iTLs, which integrates knowledge from signal processing and machine learning. We called this new methodology ClassIfication Clustering of Individual Lesions (CICIL). We used CICIL to assess similarities among the TS dynamics of 3,223 iTLs measured in 1,056 patients with metastatic colorectal cancer treated with cetuximab combined with irinotecan, in two phase II studies. We mainly observed similar dynamics among lesions within the same tumor site classification. In contrast, lesions in anatomic locations with different features showed different dynamics in about 35% of patients. The CICIL methodology has also been implemented in a user‐friendly and efficient Java‐based framework.

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A PK/PD Analysis of Circulating Biomarkers and Their Relationship to Tumor Response in Atezolizumab‐Treated non‐small Cell Lung Cancer Patients

Netterberg

Li²,

Molinero³

et al. 2018

Clin Pharma and Therapeutics

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To assess circulating biomarkers as predictors of antitumor response to atezolizumab (anti-programmed death-ligand 1 (PD-L1), Tecentriq) serum pharmacokinetic (PK) and 95 plasma biomarkers were analyzed in 88 patients with relapsed/refractory non-small cell lung cancer (NSCLC) receiving atezolizumab i.v. q3w (10-20 mg/kg) in the PCD4989g phase I clinical trial. Following exploratory analyses, two plasma biomarkers were chosen for further study and correlation with change in tumor size (the sum of the longest diameter) was assessed in a pharmacokinetic/pharmacodynamic (PK/PD) tumor modeling framework. When longitudinal kinetics of biomarkers and tumor size were modeled, tumor shrinkage was found to significantly correlate with area under the curve (AUC), baseline factors (metastatic sites, liver metastases, and smoking status), and relative change in interleukin (IL)-18 level from baseline at day 21 (RCFB ). Although AUC was a major predictor of tumor shrinkage, the effect was estimated to dissipate with an average half-life of 80 days, whereas RCFB seemed relevant to the duration of the response.

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Model-Based Prediction of Phase III Overall Survival in Colorectal Cancer on the Basis of Phase II Tumor Dynamics

Abstract: The modeling framework successfully predicted survival in a phase III trial on the basis of capecitabine phase II data in CRC. It is a useful tool to support end-of-phase II decisions and design of phase III studies.

Cited by 242 publications

References 17 publications

Preclinical Modeling of Tumor Growth and Angiogenesis Inhibition to Describe Pazopanib Clinical Effects in Renal Cell Carcinoma

Preclinical Modeling of Tumor Growth and Angiogenesis Inhibition to Describe Pazopanib Clinical Effects in Renal Cell Carcinoma

Assessing Similarity Among Individual Tumor Size Lesion Dynamics: The CICIL Methodology

A PK/PD Analysis of Circulating Biomarkers and Their Relationship to Tumor Response in Atezolizumab‐Treated non‐small Cell Lung Cancer Patients

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